Leveraging Machine Learning to Identify Patients with Social Health Needs in Emergency Departments

February 4, 2025 - 23:18

Addressing patients' health-related social needs—such as housing instability, food insecurity, transportation barriers, and financial strain—is crucial for improving overall health outcomes. However, identifying these needs within the busy environment of emergency departments can be challenging for healthcare providers. Recent advancements in machine learning are proving to be a game-changer in this area.

By analyzing vast amounts of patient data, machine learning algorithms can predict which individuals are at a higher risk of experiencing health-related social challenges. This technology enables healthcare professionals to prioritize interventions for those who may benefit the most. For instance, a patient presenting with multiple chronic conditions may also face difficulties accessing nutritious food or stable housing, which can exacerbate their health issues.

Implementing machine learning tools in emergency departments not only streamlines the identification process but also fosters a more holistic approach to patient care. By addressing the underlying social determinants of health, healthcare systems can enhance patient outcomes, reduce hospital readmissions, and ultimately create a healthier community.